Artificial intelligence for solving pediatric clinical cases: A Retrieval-Augmented approach utilizing Llama3.2 and structured references [0.03%]
基于Llama3.2和结构化参考的增强检索型人工智能解决儿科临床病例的方法
Gianluca Mondillo,Simone Colosimo,Alessandra Perrotta et al.
Gianluca Mondillo et al.
Background: The "hallucinations" of Large Language Models (LLMs) raise concerns about their accuracy in pediatrics. This study aimed to evaluate whether integrating information from the Nelson Textbook of Pediatrics throu...
Managers' perceptions and attitudes toward the use of artificial intelligence technology in selected hospital settings [0.03%]
医院管理者对人工智能技术认识及态度的调查研究
Seyed Masood Mousavi,Elahe Rahmani Samani,Mehdi Raadabadi et al.
Seyed Masood Mousavi et al.
Background: Over the past decade, artificial intelligence (AI) has transformed healthcare systems by improving cost control, clinical decision-making, and chronic disease management. This study assessed healthcare manager...
Clinical feasibility of AI Doctors: Evaluating the replacement potential of large language models in outpatient settings for central nervous system tumors [0.03%]
基于中枢神经系统肿瘤门诊场景的AI医生临床可行性研究：大语言模型的替代潜力评估
Yifeng Pan,Shen Tian,Jing Guo et al.
Yifeng Pan et al.
Background and objectives: The treatment of central nervous system (CNS) tumors is complex and resource-intensive, with higher mortality in underserved regions. Large language models (LLMs) show promise in medical support...
Reducing EHR navigation time with an advanced hyperlipidemia management Tool: An evaluation of efficiency [0.03%]
利用先进的高脂血症管理工具减少电子健康记录导航时间：效率评估
Chin-Chen Chen,Yu-Chen Liu,Guan-Ling Lin et al.
Chin-Chen Chen et al.
Background: Effective management of hyperlipidemia is crucial for preventing cardiovascular diseases. However, traditional monitoring systems often lack efficiency, particularly in navigating electronic health records (EH...
Towards a decision support system for pediatric emergency telephone triage [0.03%]
面向儿科急诊电话分诊决策支持系统的展望
Aurélia Manns,Alix Millet,Florence Campeotto et al.
Aurélia Manns et al.
Background: Telephone triage could limit admissions to emergency departments. However, telephone triage is challenging in pediatrics due to nonspecific symptoms, reliance on parental description, and emotional distress. C...
Determinants influencing medication-related alert handling in the electronic health record: A systematic review and meta-analysis [0.03%]
影响电子健康记录中与药物警戒处理的决定因素：系统评价和meta分析
Kimmy Raven,Jiaxu Zhang,Iacopo Vagliano et al.
Kimmy Raven et al.
Objective: This study aimed to identify and quantify determinants influencing prescribers' handling of medication-related computer-based alerts in electronic health records. ...
Development and external validation of an interpretable machine learning model for predicting perinatal depression in Chinese women during mid- and late pregnancy [0.03%]
一种可解释的机器学习模型在中国孕妇中期和晚期妊娠期预测围产期抑郁的开发与外部验证
Shi-Yun Wang,Jia Qiao,Rong Wang et al.
Shi-Yun Wang et al.
Objective: This study aimed to develop a machine learning (ML)-based prediction model for antenatal depression (AND) in Chinese women. Given the significant impact of AND on maternal and infant health, the goal was to cre...
Extending CARDIO:DE: Additional annotation guidelines and evaluation of NLP approaches for clinical applications [0.03%]
扩展CARDIO-DE：临床应用的NLP方法的额外注释指南和评估
Matthias Becker,Mario Krumscheid,Alisa Knobelspies et al.
Matthias Becker et al.
Background: Cardiovascular diseases are a major cause of morbidity and mortality, and the management of these conditions generates extensive clinical data. The CARDIO:DE dataset, a German-language corpus of cardiovascular...
Performance of machine and deep learning models for predicting delirium in adult ICU patients: A systematic review [0.03%]
重症监护病房成人患者谵妄预测的机器学习和深度学习模型性能系统评价
Mohammed Musaed Al-Jabri,Huda Anshasi
Mohammed Musaed Al-Jabri
Purpose: To summarize and evaluate the methodological quality of primary studies focusing on the use of machine or deep learning- based prediction models for delirium in ICU patients. ...
Comparative analysis of AI algorithms on real medical data for chronic pain detection [0.03%]
基于真实医疗数据的慢性疼痛检测人工智能算法比较分析
Carmela Comito,Agostino Forestiero,Davide Macrì et al.
Carmela Comito et al.
Background and objective: Chronic pain is a pervasive healthcare challenge with profound implications for patient well-being, clinical decision-making, and resource allocation. Traditional detection methods often rely on ...